April 2026 arXiv papers — page 60

Katharina Prasse, Steffen Jung, Isaac Bravo, Stefanie Walter

Social media platforms have become primary arenas for climate communication, generating millions of images and posts that - if systematically analysed - can reveal which communication strategies mobilise public concern and which fall flat. We aim to facilitate such research by analysing how computer vision methods can be used for social media discourse analy

Kyungtak Hong, Alexander Tsymbaliuk

We present the RLL-realization of extended orthosymplectic quantum supergroups for any parity sequence, with R-matrices evaluated in the earlier work arxiv:2408.16720. Our isomorphism is compatible with the internal structure of generalized doubles. We also relate different sign conventions through 2-cocycle twists. Furthermore, we establish a factorization

Jin-Hong Zhuang, Zhen-Hua Zhang, Yuan-Yuan Wang, Cong Pan

The ground-state properties of superheavy $Z = 122$ isotopes are investigated using the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc). Bulk properties, including binding energies, Fermi energies, nucleon separation energies, quadrupole deformations, and root-mean-square radii, are calculated. The results are compared with those obtaine

Warakorn Jindata, Trung-Phuc Vo, Chutchawan Jaisuk, Sung-Kwan Mo

In this work, we investigate and compare the electronic structures of SrTiO3 and KTaO3 under ultraviolet (UV) light induced electron doping. Using angle-resolved photoemission spectroscopy (ARPES), the evolution of the surface electronic structures of SrTiO3 and KTaO3 is systematically examined as a function of electron density. In contrast to KTaO3, SrTiO3

Hans Ole Hatzel, Ekaterina Artemova, Haimo Paul Stiemer, Evelyn Gius

We present the shared task on narrative similarity and narrative representation learning - NSNRL (pronounced "nass-na-rel"). The task operationalizes narrative similarity as a binary classification problem: determining which of two stories is more similar to an anchor story. We introduce a novel definition of narrative similarity, compatible with both narrat

Tong Wu, Chensheng Luo, Le Fang, Michael Wilczek

Statistical moments of velocity gradients provide fundamental information on the small-scale properties of turbulence. In this work, we propose a systematic method to derive exact expressions for statistical moments of arbitrary order for both longitudinal and transverse velocity gradients in isotropic turbulence. The approach is applicable to both compressi

Larissa Höfling, Matthias Tangemann, Lotta Piefke, Susanne Keller

Neuroscientists and computer vision researchers use model-brain alignment benchmarks to compare artificial and biological vision systems. These benchmarks rank models according to alignment measures such as the similarity of representational geometry or the predictability of neural responses from model activations. However, recent works have identified a num

Wancheng Zhang, Fangqi Liu, Yong Liu, Jingguo Hu

Ideal $d$-wave altermagnets with nearly orthogonal flat Fermi surfaces enable complete spin-channel separation and 100% theoretical charge--spin conversion efficiency (CSE). The metallic altermagnet $\mathrm{KV_2Se_2O}$ exemplifies this, but realistic samples host residual elliptical Fermi pockets that enhance charge conductivity while suppressing spin condu

Roman Ovsiannikov, Kurt Jacobs, Andrii G. Sotnikov, Denys I. Bondar

We present a fast, memory-efficient, unitarity-preserving numerical method beyond the rotating-wave approximation for the closed Tavis-Cummings model in which a multilevel spin system interacts with a cavity mode. This model can describe the interaction of an ensemble of spins with a cavity mode in which the spin frequency and other parameters are time-depen

Qinchen Song, Lei Zhang, Min Tang

When solving the time-dependent radiative transport equation (RTE), implicit time discretization is often employed for its robustness and stability. This results in a sequence of steady-state RTEs with identical cross-sections but varying source terms, whose repeated solution is computationally costly. To address this, we first apply the adaptive tailored fi

Rommel Salas-Guerra

This article applies postphenomenological theory to the field of cybersecurity risk management, arguing that formal risk models function as mediating artifacts that shape how security practitioners or analysts perceive, interpret, and act on threats. Based on Don Ihde's taxonomy on human-technology relationships and Peter-Paul Verbeek's extended mediational

Avinash Paliwal, Adithya Iyer, Shivin Yadav, Muhammad Ali Afridi

Precise camera control for reshooting dynamic videos is bottlenecked by the severe scarcity of paired multi-view data for non-rigid scenes. We overcome this limitation with a highly scalable self-supervised framework capable of leveraging internet-scale monocular videos. Our core contribution is the generation of pseudo multi-view training triplets, consisti

Erik Burman, Fabian Heimann

In this paper, we investigate the combination of a linear continuous interior penalty type and a non-linear artificial diffusion stabilisation applied to the transport problem, based on continuous Galerkin finite elements in space. This method was recently introduced and analysed for globally smooth solutions in [Burman 2023, SIAM J. Sci. Comput., 45, 1, A96

Lhamo Dorje, Jordan Madden, Soamar Homsi, Xiaohua Li

Near-field millimeter-wave (mmWave) imaging is widely deployed in safety-critical applications such as airport passenger screening, yet its own security remains largely unexplored. This paper presents a systematic study of the adversarial robustness of mmWave imaging algorithms under waveform-domain physical attacks that directly manipulate the image reconst

Dustin Vivod, Binny A. Davis, Tobias Binninger, Michael Eikerling

Electrocatalysis is greatly influenced by the local reaction environment, which is governed by the structure of the catalyst, the distribution of the electrolyte, and the local electric field. In catalytic systems comprised of complex molecular species like ionomers, the distribution of electrolyte can vary substantially, resulting in divers local reaction e

Yingkai Yang, Chaoqi Chen, Hui Huang

Test-Time Adaptation (TTA) aims to mitigate distributional shifts between training and test domains during inference time. However, existing TTA methods fall short in the realistic scenario where models face both continually changing domains and the simultaneous emergence of unknown semantic classes, a challenging setting we term Open-set Continual Test-Time

Binhang Qi, Yun Lin, Xinyi Weng, Chenyan Liu

Test cases are essential for software development and maintenance. In practice, developers derive multiple test cases from an implicit pattern based on their understanding of requirements and inference of diverse test scenarios, each validating a specific behavior of the focal method. However, producing comprehensive tests is time-consuming and error-prone:

Minji Jung, Minjae Lee, Yejin Kim, Sarang Choi

LLM leaderboards are widely used to compare models and guide deployment decisions. However, leaderboard rankings are shaped by evaluation priorities set by benchmark designers, rather than by the diverse goals and constraints of actual users and organizations. A single aggregate score often obscures how models behave across different prompt types and composi

Zhiyong Qiu, Cheng Li, Jinzhen Yang, Fangkun Sun

The Sabatier reaction, the catalytic hydrogenation of CO2 into CH4, offers a cornerstone for carbon capture and utilization, and in-situ resource utilization during space exploration; however, it faces a fundamental thermodynamic-kinetic paradox: although highly exothermic, conventional catalysts still require continuous external heating to activate CO2 and

Breno Matos, Rennan C. Lima, Savvas Zannettou, Fabricio Benevenuto

Online misinformation is one of the most challenging issues lately, yielding severe consequences, including political polarization, attacks on democracy, and public health risks. Misinformation manifests in any platform with a large user base, including online social networks and messaging apps. It permeates all media and content forms, including images, tex

Tasnim Kabir, Dmytro Kurdydyk, Aadi Palnitkar, Liam Dorn

Existing audio question answering benchmarks largely emphasize sound event classification or caption-grounded queries, often enabling models to succeed through shortcut strategies, short-duration cues, lexical priors, dataset-specific biases, or even bypassing audio via metadata and captions rather than genuine reasoning Thus, we present AUDITA (Audio Unders

Hao Chen, Arnab Phani, Sebastian Schelter

Data is a central resource for modern enterprises, and data validation is essential for ensuring the reliability of downstream applications. However, existing automated data unit testing frameworks are largely task-agnostic: they validate datasets without considering the semantics and requirements of the code that consumes the data. We present PrismaDV, a co

Guangxiang Zhao, Qilong Shi, Xusen Xiao, Xiangzheng Zhang

Reasoning LLMs often spend substantial tokens on long intermediate reasoning traces (e.g., chain-of-thought) when solving new problems. We propose to summarize and store reusable reasoning skills distilled from extensive deliberation and trial-and-error exploration, and to retrieve these skills at inference time to guide future reasoning. Unlike the prevaili

Yasmine Meroz

Plants solve complex problems without centralized control, relying instead on growth-driven dynamics to sense, navigate, and optimize resource acquisition. This review presents a unified physical framework for understanding plant behavior through three complementary principles: distributed physical computation, embodied mechanical intelligence, and functiona

Zahoor Iqbal Bhat, S. Pirzada

Let $G$ be a connected graph of order $n$. A $\{P_3,P_4,P_5\}$-factor is a spanning subgraph $H$ of $G$ such that every component of $H$ is isomorphic to an element of $\{P_3,P_4,P_5\}$. In this paper, we establish a sufficient condition on the size of the graph $G$ with minimum degree $\delta$ to have a $\{P_3, P_4, P_5\}$-factor. Subsequently, we provide a

Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu, Chin Chun Ooi

Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical laws into the learning process, PINN models have demonstrated the ability to learn physical outcomes reasonably well. However, current PINN a

Timothy Joseph Murphy, Jennifer Cook, Hélio Clemente José Cuve

Deepfake detection research has largely converged on deep learning approaches that, despite strong benchmark performance, offer limited insight into what distinguishes real from manipulated facial behavior. This study presents an interpretable alternative grounded in bio-behavioral features of facial dynamics and evaluates how computational detection strateg

Kasper H. Nielsen, Etienne Corminboeuf, Benedikt Tissot, Love A. Pettersson

High-quality photonic Bell state measurements (BSMs) enable scalable universal quantum computing and long distance quantum communication. However, when implemented with linear optics, BSMs are fundamentally probabilistic, introducing substantial hardware overheads and limiting noise tolerance in photonic quantum computing architectures. Nonlinear interaction

Nils Lid Hjort

The linear hazard regression model developed by Aalen is becoming an increasingly popular alternative to the Cox multiplicative hazard regression model. There are no methods in the literature for selecting among different candidate models of this nonparametric type, however. In the present paper a focused information criterion is developed for this task. The

Dingke Tang, Xuming He, Shu Yang

Mendelian randomization is a powerful tool for causal inference in observational studies. The two-sample summary-data design, which estimates genetic associations with exposures and outcomes in separate cohorts, is the most widely used Mendelian randomization approach in large-scale genomic studies. However, this approach relies on a strong assumption of pop

Gossrin Jean-Marc Bomisso, Ali Ouattara Kouma, Marie Esther Anassé

We study a nonlinear coupled system of partial differential equations arising from thermo--reaction--phase models. The system combines a heat diffusion equation, temperature-dependent chemical reactions of Arrhenius type, and a phase variable, and is formulated as a strongly coupled parabolic problem with homogeneous Neumann boundary conditions. We first est

Shi-Cheng Liu, Lei-Hua Liu, Bichu Li, Hai-Qing Zhang

In this work, we systematically investigate the quantum-information diagnostics of cosmological perturbations with a nontrivial sound speed, utilizing a normalized open two-mode squeezed-state framework. Rather than introducing new observables, our analysis focuses on how a modified sound speed dynamically reshapes the Schr\"odinger evolution of the squeezin

Margot Fitz Axen, Stella S. R. Offner, Philip F. Hopkins, Michael Y. Grudić

Cosmic rays (CRs) drive ionization and influence gas dynamics in molecular clouds (MCs), potentially impacting the resulting star formation outcomes. Although previous simulations of individual star formation have included methods for cosmic ray transport (CRT), none have been large enough to resolve the stellar initial mass function (IMF). We conduct numeri

Matteo Rigoni, Daniele Lanzoni, Francesco Montalenti, Roberto Bergamaschini

Simulations of crystal growth are performed by using Convolutional Recurrent Neural Network surrogate models, trained on a dataset of time sequences computed by numerical integration of Allen-Cahn dynamics including faceting via kinetic anisotropy. Two network architectures are developed to take into account the effects of a variable supersaturation value. T

Anita Gjesteland, Sigrun Ortleb, Salim Elghawi, David C. Del Rey Fernández

We develop an unconditionally energy-stable tensor-product space-time discretization framework for the solution of a linear kinetic transport equation in one space dimension. The kinetic equation is a simplified model of radiative transfer formulated as a hyperbolic balance law in diffusive scaling for a particle distribution function of the independent vari

Joseba Fernandez de Landa, Carla Perez-Almendros, Jose Camacho-Collados

LLMs have limitations when it comes to cultural coverage and competence, and in some cases, show specific cultural biases. Although prior studies have examined the cultural capabilities of LLMs, none have specifically investigated their regional preferences in generic culture-related questions. In this work, we propose a new dataset based on a comprehensive

Anas Buhayh, Elizabeth McKinnie, Clement Canel, Robin Burke

Optimizing outcomes for multiple stakeholders in recommender systems has historically focused on algorithmic interventions, such as developing multi-objective models or re-ranking results from existing algorithms. However, structural changes to the recommendation ecosystem itself remain understudied. This paper explores the implications of algorithmic plural

Markus Schütz, Lukas Lipp, Elias Kristmann, Michael Wimmer

Previous work shows that small triangles can be rasterized efficiently with compute shaders. Building on this insight, we explore how far this can be pushed for massive triangle datasets without the need to construct acceleration structures in advance. Method: A 3-stage rasterization pipeline first rasterizes small triangles directly in stage 1, using atomic

Buqiang Xu, Yijun Chen, Jizhan Fang, Ruobin Zhong

Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasoning and multi-hop question answering. Current approaches face a fundamental trade-off: flat memory is efficient but fails to model relational structure, while graph-based memory enables structured reasoning at th

Luca Giannoni, Uzair Hakim, Fréderic Lange, Musa Talati

Optical imaging and spectroscopy solutions, such as near-infrared spectroscopy (NIRS) and diffuse optical tomography (DOT), have the potential to provide compact, bedside monitoring of the placenta in the clinic, thanks to recent advancements in miniaturisation and wireless wearability. This would provide neonatologist with continuous assessment of the pregn

Krishna Narasimhan

Large language models are increasingly used to make static analysis tools accessible through natural language, yet existing systems differ in how much they delegate to the LLM without treating the degree of delegation as an independent variable. We compare three architectures along a spectrum of LLM involvement for translating natural language to Joern's que

Yuli Wu

This note provides a chronological account of Fr\'echet distances, starting with Maurice Fr\'echet's 1906 doctoral thesis on distances in abstract sets and tracing the Fr\'echet distance between polygonal curves and its algorithmic computation in the 1990s. It then continues with his 1957 paper on a coupling-based distance between probability laws with a bri

Dat To-Thanh, Nghia Nguyen-Trong, Hoang Vo, Hieu Bui-Minh

Image enhancement models for mobile devices often struggle to balance high output quality with the fast processing speeds required by mobile hardware. While recent deep learning models can enhance low-quality mobile photos into high-quality images, their performance is often degraded when converted to lower-precision formats for actual use on mobile phones.

Lydie Bednarczyk, Jamil Zaghir, Julien Ehrsam, Maria Tcherepanova

Objectives: Large language models (LLMs) are increasingly used for clinical text summarization, yet structured methods to assess associated patient safety risks remain limited. Failure Mode, Effects, and Criticality Analysis (FMECA) provides a proactive framework for systematic risk identification but has not been adapted to LLM-generated clinical content. T

Jing Hu, Wending Huang, Tianjian Lv, Ming Yan

Light detection and ranging (LiDAR) is widely used in autonomous systems and industrial metrology; however, the simultaneous acquisition of three-dimensional (3D) structure and broadband spectral information remains challenging, as conventional hyperspectral LiDAR relies on wavelength-scanning or spectrometer-based detection that limits speed. Here, we demon

Yaxuan Li, Zhongyi Zhou, Yefei Chen, Yanjiang Guo

Post-training is essential for turning pretrained generalist robot policies into reliable task-specific controllers, but existing human-in-the-loop pipelines remain tied to physical execution: each correction requires robot time, scene setup, resets, and operator supervision in the real world. Meanwhile, action-conditioned world models have been studied main

Liam P. Burns, Dayse M. Cavalcanti, Felipe G. Cabral, Max H. de Queiroz

Discrete-event systems and supervisory control theory provide a rigorous framework for specifying correct-by-construction behavior. However, their practical application to swarm robotics remains largely underexplored. In this paper, we investigate a topological recovery method based on discrete-event-systems within a swarm robotics context. We propose a hybr

Ylea Vlamidis, Stiven Forti, Antonio Rossi, Arrigo Calzolari

Two-dimensional (2D) magnetic materials integrated with graphene offer a compelling platform for next-generation spintronic devices, yet nickel in its 2D form remains largely unexplored, due to fundamental synthesis limitations. Here, we report the controlled intercalation of Ni beneath epitaxial graphene on the Si-face of SiC(0001), achieved through a scala

Christopher P. Bendel, Daniel K. Nakano, Cornelius Pillen

Let $G$ be a reductive algebraic group scheme defined over ${\mathbb F}_{p}$ and $k$ be an algebraically closed field of characteristic $p$. There are two associated families of finite group schemes, the $r$-th Frobenius kernels, denoted by $G_r$, and the fixed points of the iterated Frobenius map, the finite groups of Lie type, denoted by $G(\mathbb{F}_q).$

Sara Mulas, Alberto Pellizzoni, Marco Marongiu, Adriana Marcucci

High radio frequencies observations with the Italian network of large single-dish radio telescopes resulted in ~450 solar images between 2018 and 2023 in K-band frequency range (18-26 GHz). Solar radio mapping at these frequencies allows the probing of the Active Regions (ARs) chromospheric magnetic field close to the Transition Region, where strong flares a

Shubhashis Mallik, Gaurav Narain

Gravitational path-integral over $\mathbb{R}\times S^3$ complex metrics with fluctuations is studied in 4D for Einstein-Hilbert gravity in Lorentzian signature, with the aim to investigate the IR properties of complex saddles for various boundary choices. General covariance doesn't allow arbitrary boundary choices for the background and fluctuations. In the

Xinyu Wang, Tao Yu, Chunlin Wang, Pengfei Li

Testing the homogeneity of two distributions is fundamental in statistics, but classical procedures may fail under nonignorable nonresponse. In many surveys, callback data record repeated contact attempts and provide auxiliary information about the response mechanism. We develop a semiparametric framework for two-sample homogeneity testing that explicitly in

Nikeethan Selvaratnam, Dorinel Bastide, Clément Fernandes, Wojciech Pieczynski

Predicting future operational risk losses gives rise to a significant challenge due to the heterogeneous and time-dependent structures present in real-world data. Furthermore, stress test exercises require examining the relationship with operational losses. To capture such relationship, we propose to use an extension of Hidden Markov Models to multivariate o

Yiran Du, Huimin He

The increasing integration of artificial intelligence (AI) in higher education has raised important questions regarding students' transparency in reporting AI-assisted work. This study investigates the psychological mechanisms underlying university students' willingness to disclose AI use by applying the Cognition--Affect--Conation (CAC) framework. A sequent

Yuan Chai

We use enhanced Langlands parameters to obtain a classification for irreducible representations of twisted $p$-adic general linear groups in unramified principal series. We give the definition of standard representations and prove the twisted Kazhdan-Lusztig conjecture for the multiplicities in the Grothendieck group. We mainly follow Lusztig's work in the c

A. Cheema, V. S. Veena, K. M. Menten, T. S. Pillai

We investigated the high-mass star formation activity in a subregion of the Sagittarius E star-forming complex, centered at (l,b) = (358.69 deg, 0.03 deg), where infrared and radio sources trace a prominent U-shaped structure that has not been identified in previous studies. We used radio continuum data from the Global View on Star Formation (GLOSTAR) survey

Minjo Park, Metin Sitti

Pumping fluids is fundamental to a wide range of industrial, environmental, and biomedical applications. Among various pumping mechanisms, peristaltic pumps enable efficient and safe fluid transport by deforming an elastic tube without direct contact with the working fluid. Although previous studies have introduced mechanical, pneumatic, or magnetic actuatio

Wenxuan Bao, Yanjun Zhao, Xiyuan Yang, Jingrui He

Pretrained vision-language models such as CLIP exhibit strong zero-shot generalization but remain sensitive to distribution shifts. Test-time adaptation adapts models during inference without access to source data or target labels, offering a practical way to handle such shifts. However, existing methods typically assume that test samples come from a single,

Pêdra Andrade, Verena Bögelein, Frank Duzaar, Kristian Moring

We establish local Calder\'on-Zygmund type estimates for weak solutions to nonlinear parabolic systems with $p$-growth and VMO coefficients. In particular, we prove that if the right-hand side belongs locally to $L^{\mu s}$, where the exponent $\mu$ depends explicitly on $p$, $N$, and a prescribed target exponent $s>p$, then the spatial gradient of the solut

Manuka Suriyage, Hao Qin, Xueqian Sun, Wenkai Yang

Monolayers of transition metal dichalcogenides (TMDCs), known for their strong excitonic states with high binding energies in the visible spectrum at room temperature, offer great potential for polariton-driven devices. While polariton guided modes in bulk TMDCs have been reported the real space experimental observation of 2D exciton-polariton guided modes i

Wujiang Xu, Jiaojiao Han, Minghao Guo, Kai Mei

LLM Agents Harnesses are hand-designed and stay fixed, so agents accumulate experience but never learn how to use it: which memories to retrieve, when retrieved evidence is misleading, and when the retrieval strategy itself should change. We introduce Agent Evolving Learning (AEL), a two-timescale framework that evolves the harness, recasting memory use as o

Yilong Chen, Yanxi Xie, Zitian Gao, He Xin

Large token-indexed lookup tables provide a compute-decoupled scaling path, but their practical gains are often limited by poor parameter efficiency and rapid memory growth. We attribute these limitations to Zipfian under-training of the long tail, heterogeneous demand across layers, and "slot collapse" that produces redundant embeddings. To address this, we

Yanis Le Fur, Diego Martín-Cano, Carlos Sánchez Muñoz

Quantum technologies in the terahertz (THz) require a coherent interface between addressable qubits and THz quantum channels -- a capacity that so far, remains largely underdeveloped. Here, we propose and demonstrate the generation of steady-state entanglement between polar quantum emitters, mediated by THz photons. We exploit strong visible-light driving of

Bruno Avritzer, Nathan Sankary

Modular quantum computing architectures require error correction schemes that remain effective in the presence of noisy inter-processor operations. As such, minimizing the number of such operations on logical circuits partitioned across quantum processors is a primary objective of distributed quantum computing. In this work, we develop basic techniques for q

Salvador V. Balkus, Christian Testa, Nima S. Hejazi

As research in causal inference has sought to address more complex scientific questions, the number of specialized estimands in the field has proliferated. Recognition that many of these estimands share a common linear form has generated interest in simplifying estimation procedures using Riesz representers. In this work, we construct a targeted minimum loss

Benjamin Klopsch, Margherita Piccolo, Britta Späth

The representation zeta function of a profinite group $G$ encodes the distribution of continuous irreducible complex representations of $G$ as a function of the dimension. Its abscissa of convergence $\alpha(G)$ describes the polynomial degree of representation growth of $G$. Within the class of quasi-semisimple profinite groups, we characterise those of pol

Gang Chen, Daozhi Han, Jiaxuan Liu, Yangwen Zhang

We propose a hybridizable discontinuous Galerkin (HDG) method combined with convex-concave splitting for the temporal discretization of the convective Cahn-Hilliard equation. The convection term is discretized explicitly without stabilization, yielding three key advantages: (1) unconditional stability, (2) preservation of the optimal convergence rate for pie

Minh Duc Bui, Xenia Heilmann, Mattia Cerrato, Manuel Mager

Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming and reveal solely overt, explicitly encoded bias. We demonstrate that this approach dramatically underestimates bias in practice by examining a more realistic task: generating machine learning (ML) pipelines

Guangkai Xu, Hua Geng, Huanyi Zheng, Songyi Yin

Feed-forward visual geometry estimation has recently made rapid progress. However, an important gap remains: multi-frame models usually produce better cross-frame consistency, yet they often underperform strong per-frame methods on single-frame accuracy. This observation motivates our systematic investigation into the critical factors driving model performan

Michelle Seng Ah Lee, Kirtan Padh, David Watson, Niki Kilbertus

Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural inequalities, but they can support socio-technical decision systems by surfacing discriminatory biases, clarifying trade-offs, and enabling governance. Although fairness is well st

M. Pelkner, K. Hallatschek, M. Raeth

Simulating fully kinetic, two-species plasmas is computationally challenging due to the stiff multiscale dynamics of electrons and ions. While enforcing a quasi-neutral time evolution mitigates this stiffness, it requires an electric potential that consistently maintains this constraint. In this work, we present an implicit approach to determine this electri

Isaac Pérez Castillo

Experimental science usually relies on laboratory procedures that, after finitely many steps, terminate with numerical reports on physical quantities. This paper argues that such procedures can be understood as algorithmic once the protocol, background conditions, and reporting rules are fixed. Assuming an explicit physical Church--Turing bridge principle, a

William Hunt, Aleksandra Landowska, Horia A. Maior, Sarvapali D. Ramchurn

Real-world deployments of human--swarm teams depend on balancing operator workload to leverage human strengths without inducing overload. A key challenge is that swarm size is often dynamic: robots may join or leave the mission due to failures or redeployment, causing abrupt workload fluctuations. Understanding how such changes affect human workload and perf

Bernard Muller, Antonio Armando Ortiz Barrañón, LaVonne Roberts

We previously introduced a training-free method for dysarthria severity assessment based on d-prime separability of phonological feature subspaces in frozen self-supervised speech representations, validated on 890 speakers across 5 languages with HuBERT-base. Here, we scale the analysis to 3,374 speakers from 25 datasets spanning 12 languages and 5 aetiologi

Suroj Dey, Peter Barker, Animesh Datta

We show that detecting steady-state Coulomb-mediated reduction in the thermal variance of the differential motional mode of two nanospheres can bound the Continuous Spontaneous Localization (CSL) parameter ($\lambda_{{\text{CSL}}}$). For realistic experimental parameters, the resulting bounds are comparable to those obtained from X-ray emission experiments a

Shounian Deng, Weiyin Fei, Banban Shi

Most existing literature focuses on pointwise convergence (i.e., convergence at a fixed time point) of numerical solutions for Stochastic functional differential equations (SFDEs). In contrast, this paper investigates the strong segment convergence (i.e., the strong order of convergence of the numerical segment process). For SFDEs with super-linear drift and

Wille Liu, Wei-Hsuan Hsin, Cheng-Chiang Tsai

We prove that on a semisimple Lie algebra $\mathfrak{g}$ over a finite field of large characteristic, if a complex-valued invariant function $f$ and its Fourier transform $\hat f$ are both supported in the nilpotent cone of $\mathfrak{g}$, then $\hat f = \gamma^{-1}f$ for an explicit quadratic Gauss sum $\gamma$. Consequently, we determine a fourth root of u

Michalis Chatzittofi, Yuto Hosaka, Andrej Vilfan, Ramin Golestanian

Autonomous and driven transport in chiral active fluids have been shown to exhibit features that cannot be accommodated within the classical formulation of fluid mechanics, due to the role of odd viscosity. We generalize the theory of phoretic active matter to fluid environments with odd viscosity and derive expressions for translational and rotational self-

Luis Barba, Johannes Kirschner, Benjamin Bejar

Computed Tomography (CT) is a widely used imaging modality in medical and industrial applications. To limit radiation exposure and measurement time, there is a growing interest in sparse-view CT, where the number of projection views is significantly reduced. Deep neural networks have shown great promise in improving reconstruction quality in sparse-view CT,

Jiali Wei, Ming Fan, Guoheng Sun, Xicheng Zhang

The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent studies have demonstrated the feasibility of backdoor attacks against LLMs. However, existing methods suffer from three key shortcomings: explicit trigger patterns that compromise naturalness, unreliable injection of

Laura Duits, Bouazza El Moutaouakil, Ivano Malavolta

Context. The most used development framework for robotics software is ROS2. ROS2 architectures are highly complex, with thousands of components communicating in a decentralized fashion. Goal. We aim to evaluate how LLMs can assist in the comprehension of factual information about the architecture of ROS2 systems. Method. We conduct a controlled experiment wh

Aaron Brunk, Dennis Höhn

This work presents a conforming finite-element scheme for the non-isothermal Allen-Cahn-Navier-Stokes system, incorporating periodic, closed, and thermal boundary conditions. The system comprises the incompressible Navier-Stokes equations coupled with the non-isothermal Allen-Cahn equation, which includes a non-conserved phase-field equation and a temperatur

Liane Vogel, Kavitha Srinivas, Niharika D'Souza, Sola Shirai

Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic search and table-based prediction. Despite the growing number of such models, it remains unclear which approach works best in practice, as existing methods are often evaluated under t

Paolo Viviani, Fabrizio Bertone, Giacomo Vitali, Emanuele Dri

We describe the design, implementation, and nine-month operational experience of the software management stack for Lagrange, an IQM Spark five-qubit superconducting quantum computer jointly acquired by LINKS Foundation, Politecnico di Torino and the Italian National Institute of Metrological Research (INRiM), and managed by LINKS. Lagrange is, to our knowled

Ilir Gusija, Fady Alajaji, Serdar Yüksel

Simultaneous localization and mapping (SLAM) is a foundational state estimation problem in robotics in which a robot accurately constructs a map of its environment while also localizing itself within this construction. We study the active SLAM problem through the lens of optimal stochastic control, thereby recasting it as a decision-making problem under part

Mikael Vartiainen, Ilari Lilja, Ekaterina Mukhanova, Kirill Petrovnin

The creation of high-quality cluster states in superconducting microwave circuits is a relevant ingredient in continuous-variable quantum computing. Although large-scale cluster states have been established in optical systems, dissipation prevents their direct applicability to the microwave realm. Recent improvements in superconducting parametric circuits, i

Jamie Simon, Daniel Kunin, Alexander Atanasov, Enric Boix-Adserà

In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull together major strands of ongoing research in deep learning theory and identify five

Rizal Maulana, Ádám Rák, Sándor Földi, György Cserey

Continuous monitoring of physiological signals is essential for the early detection of health problems. A measurement system that ensures high sensitivity, accuracy, and user comfort is needed. In this study, we designed and optimized a flexible piezoresistive yarn (FPY) sensor to achieve a high sensitivity and wide working range for detecting physiological

Noah Jaffe, John Ashley Burgoyne

This paper introduces PHOTON (PHysical Optical Tracking of Notes), a non-invasive optical sensing system for measuring key-lever motion in historical keyboard instruments. PHOTON tracks the vertical displacement of the key lever itself, capturing motion shaped by both performer input and the instrument's mechanically imposed, time-varying load. Reflective op

Rawal Khirodkar, He Wen, Julieta Martinez, Yuan Dong

We present Sapiens2, a model family of high-resolution transformers for human-centric vision focused on generalization, versatility, and high-fidelity outputs. Our model sizes range from 0.4 to 5 billion parameters, with native 1K resolution and hierarchical variants that support 4K. Sapiens2 substantially improves over its predecessor in both pretraining an

Aytijhya Saha, Aaditya Ramdas

E-variables enable safe and anytime-valid inference, with log-optimal e-variables given by the likelihood ratio of the least favorable distributions (LFDs) when they exist in composite settings. While this unconstrained theory is well understood, one may need/wish to impose additional structural constraints, including differential privacy, quantization, boun

Francis Hahn, Mohd Mamoon, Alexandru G. Bardas, Michael Collins

Technology for security operations centers (SOCs) has a storied history of slow adoption due to concerns about trust and reliability. These concerns are amplified with artificial intelligence, particularly large language models (LLMs), which exhibit issues such as hallucinations and inconsistent outputs. To assess whether LLM-based tools can improve SOC effi

Jonathan Osinski, Trevor Wilson

We study compactness and L\"owenheim-Skolem properties of fragments of the class-sized logic $\mathcal{L}_{\infty \infty}$ and of class-sized versions of second-order and sort logics. In these fragments, certain combinations of infinitary quantifiers and boolean connectives are banned. While model-theoretic properties fail for unrestricted class logics, this

Eylon E. Krause

The choice of activation function plays a crucial role in the optimization and performance of deep neural networks. While the Rectified Linear Unit (ReLU) remains the dominant choice due to its simplicity and effectiveness, its lack of smoothness may hinder gradient-based optimization in deep architectures. In this work we propose a family of $C^{2N}$-smooth

Spencer Wadsworth, Nabin Koirala, Nicole Landi, Ofer Harel

Detecting shared neural activity from functional magnetic resonance imaging (fMRI) across individuals exposed to the same stimulus can reveal synchronous brain responses, functional roles of regions, and potential clinical biomarkers. Intersubject correlation (ISC) is the main method for identifying voxelwise shared responses and per-subject variability, but

Xin Song, Kaiyuan Li, Jinxin Hu

Sales promotions, as short-term incentives to stimulate product purchases, play a pivotal role in modern e-commerce marketing strategies. During promotional events, user behavior patterns exhibit distinct characteristics compared to regular periods. In the pre-promotion phase, users typically engage in product search and browsing without immediate purchases,

Juan J. Forero-Hernańdez, Francisco Guillén-González, Élder J. Villamizar-Roa

We propose and analyze an optimal control problem associated with a Keller-Segel type parabolic system with chemoattraction, modeling the glioblastoma growth in a bi-dimensional bounded domain, influenced by the presence of oxygen where the controls are two different (chemotherapy and antiangiogenic) therapies. The model considers the random diffusion of tum

Yiqi Chen, Holger Boche, Marc Geitz

This paper studies the hierarchical joint source-channel coding with information leakage constraint in the first-phase reconstruction and distortion constraints. The receiver's access to the data varies and is evaluated by the quality of the side information. Due to the consideration of channel capacity limitation or the efficiency of the system performance,

Irene Aldridge, Jolie An, Riley Burke, Michael Cao

The emergence of agentic artificial intelligence (AI) represents a fundamental transformation in financial markets, characterized by autonomous systems capable of reasoning, planning, and adaptive decision-making with minimal human intervention. This comprehensive survey synthesizes recent advances in agentic AI across multiple dimensions of financial operat

Andronikos Paliathanasis, Kevin J. Duffy

We introduce a family of phenomenological cosmological models featuring an interacting dark sector modulated by a sparseness scale parameter, in order to describe the late-time accelerated expansion of the universe. The sparseness scale, inspired by well-established saturation mechanisms in ecology and biology, is introduced in the interaction as a half-satu